# Type of tasks [[tasks]]

A task is an **instance** of a Reinforcement Learning problem. We can have two types of tasks: **episodic** and **continuing**.

## Episodic task [[episodic-task]]

In this case, we have a starting point and an ending point **(a terminal state). This creates an episode**: a list of States, Actions, Rewards, and new States.

For instance, think about Super Mario Bros: an episode begins at the launch of a new Mario Level and ends **when you’re killed or you reached the end of the level.**

<figure>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/mario.jpg" alt="Mario">
<figcaption>Beginning of a new episode.
</figcaption>
</figure>


## Continuing tasks [[continuing-tasks]]

These are tasks that continue forever (**no terminal state**). In this case, the agent must **learn how to choose the best actions and simultaneously interact with the environment.**

For instance, an agent that does automated stock trading. For this task, there is no starting point and terminal state. **The agent keeps running until we decide to stop it.**

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/stock.jpg" alt="Stock Market" width="100%">

To recap:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/tasks.jpg" alt="Tasks recap" width="100%">
